Trusted by Europe's leading organizations
Deploying secure AI solutions that integrate directly with your core production systems. DNA Solutions builds private RAG, custom LLM integrations, and document classification models that run inside your existing infrastructure.
Trusted by Europe's leading organizations
DNA Solutions builds AI that runs inside your own environment, integrated with the systems you already operate including Oracle and SAP. Our work starts from the data layer: pipelines, retrieval and document structure first, then the model. On the Canon document classification project, a custom model reached 94.7% accuracy against 84.2% on Azure AI Document Intelligence.
DNA Solutions designs technology that lands on your bottom line. European enterprises trust us with extreme data volumes and critical financial pipelines.
See client resultsDNA Solutions built and maintains a Deloitte-audited billing platform processing €300M in audited transactions every month.
By optimizing software licensing fees for a major European organization, DNA Solutions delivered over €1M in yearly cost savings.
A senior team of engineers and consultants across Europe.
T-Systems, Satellic, European Commission: our longest engagements last because we deliver.
Getting a model into production is mostly the work that happens before and after it. These are the steps we follow, in order.
Custom classification and extraction models for enterprise documents. The Canon project used a custom model that reached 94.7% accuracy against 84.2% on Azure AI Document Intelligence, deployed in a private cloud environment. We build these models on your real document mix rather than a generic baseline, so the accuracy you see in the benchmark is the accuracy you get in production. Extraction maps each document type to your downstream systems, with a confidence threshold that routes uncertain cases to human review.
Machine learning models for operational forecasting, built on top of consolidated data pipelines. We treat the data layer first, then the model, because a forecasting model is only as reliable as the pipeline feeding it. Models are trained, validated and monitored on your own historical data, and stay inside your environment alongside the operational systems they support. We monitor accuracy drift over time and retrain on a defined cadence, so the model keeps pace with how your operations actually evolve.
We integrate AI into the systems you already operate, including Oracle and SAP, rather than adding standalone tools alongside them. Models connect to your data sources and internal applications through stable, documented interfaces, so AI becomes part of the production path instead of a parallel experiment. This keeps governance, access control and audit lineage consistent with the rest of your stack. Each integration ships with monitoring and fallback behaviour, so a model that is slow or unavailable degrades cleanly without blocking the workflow around it.
For regulated industries, deployment happens inside your environment under GDPR and EU AI Act constraints, with no dependency on external LLM providers in the production path. Documents and embeddings stay in your infrastructure, with no data sent to external LLM APIs. Where it fits, we run open-weight models on private cloud so you keep full control of where data lives and which model version is in production.
DNA Solutions engineers private AI that runs inside your infrastructure and integrates with the systems you already operate. From retrieval-augmented generation to custom model integration, our work starts from the data layer.
What we buildRetrieval-augmented generation deployed inside your infrastructure. Your documents and embeddings stay in your environment, with no data sent to external LLM APIs. Suited to regulated sectors under GDPR and EU AI Act constraints.
Integration of language models into your existing applications and workflows. We connect models to your data sources, Oracle and SAP systems, and internal tools, with the option to run open-weight models on private cloud rather than third-party APIs.
Scoping and feasibility for enterprise AI projects. We assess your current stack, identify where AI adds measurable value, and define the data and infrastructure work required before any model is built.
The same private-deployment and integration core sits under very different inputs in telecom, retail and tolling. We fit it to each sector's documents and constraints.
Document classification, OSS/BSS data integration, and predictive analytics for telecom operators. Engagements include T-Systems and Deutsche Telekom.
AI customer segmentation and demand forecasting for retail and distribution operations, built on consolidated data pipelines.
Predictive analytics and document processing for toll and road operators, integrated with existing data platforms.
How we deploy private AI and machine learning models inside the infrastructure of European enterprises.
A private document classification engine that beat Azure AI Document Intelligence on two of three benchmark datasets, 94.7% versus 84.2%.
94.7% vs 84.2% on Azure AI
A semantic matching engine that goes beyond keywords, ranking candidates against roles using vector search.
Vector search, beyond keywordsSenior decision-makers on the data, integration and platform engagements DNA Solutions has delivered.
"We collaborated on an innovative recruiting app, and what stood out most was the supportive atmosphere and the strong autonomy given to every team member."
"DNA works with us to deliver digital systems at scale so that we can serve our customers digitally. They are both reactive to requests and proactive with ideas and proposals."
"The real connection between sales and delivery is what sets them apart. Most IT companies have salespeople disconnected from the people actually building the solution. At DNA, that's simply not the case."
The questions we get most when AI has to run inside a regulated environment.
Yes. DNA Solutions builds private deployments that run inside your environment, including private RAG where your documents and embeddings stay in your infrastructure, with no data sent to external LLM APIs. This keeps your data under your own governance and lets us deploy under GDPR and EU AI Act constraints. Where it fits, we run open-weight models on your private cloud rather than calling third-party APIs, so you control which model version is in production and where data lives. Retrieval runs against your own document store, and access follows the same permissions as the source systems, so a user only ever sees what they are already entitled to. To start, we typically scope a contained use case, prove the accuracy and data boundaries, then widen from there.
Yes. We integrate language models and machine learning into the systems you already operate, including Oracle and SAP, rather than adding standalone tools alongside them. Models connect to your data sources and internal applications through stable, documented interfaces, so AI becomes part of your production path instead of a parallel experiment. Our team brings deep experience integrating enterprise data platforms across European telecom and infrastructure clients, which lets us connect models to legacy estates without disrupting the systems on top of them. We treat the integration layer as a product in its own right, with versioned interfaces and monitoring, so a model can be updated or replaced without rewiring the systems around it. The data layer comes first: we consolidate the inputs a model depends on before connecting it.
On the Canon document classification project, a custom model built by DNA Solutions reached 94.7% accuracy against 84.2% on Azure AI Document Intelligence, outperforming the hyperscaler baseline on 2 of 3 enterprise document datasets. The gap comes from building on your real document mix rather than a generic baseline: a model trained and tuned on your own data and document structure tends to outperform a general-purpose hyperscaler service on the documents that actually matter to your operations. A hyperscaler service is built to be average across every customer, while a custom model is fitted to the formats and edge cases you actually run. It also stays inside your environment rather than calling an external API, so the accuracy gain comes with the data control a regulated operation needs.
DNA Solutions builds private deployments for GDPR and EU AI Act constraints, with no dependency on external LLM providers in the production path. Data stays inside your environment, model versions are pinned and documented, and the integration layer keeps audit lineage consistent with the rest of your stack, so an external auditor can trace which model and data produced a given output. Data sovereignty is the starting point: documents and embeddings remain in your infrastructure under your own access control, never leaving for a third-party API. The EU AI Act adds obligations around documentation, traceability and human oversight depending on how a system is classified, and pinned versions with full lineage make those obligations auditable. For regulated industries, this is the difference between an experiment and a system you can run in production.